From ed47b5ff016b45643e13f24da427df1d8252de6d Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Tue, 19 Mar 2019 20:47:28 -0500
Subject: [PATCH] Adds a symmetric log uniform prior
---
bilby/core/prior.py | 80 +++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 80 insertions(+)
diff --git a/bilby/core/prior.py b/bilby/core/prior.py
index 4ab67c87c..fe86213a8 100644
--- a/bilby/core/prior.py
+++ b/bilby/core/prior.py
@@ -770,6 +770,86 @@ class LogUniform(PowerLaw):
logger.warning('You specified a uniform-in-log prior with minimum={}'.format(self.minimum))
+class SymmetricLogUniform(Prior):
+
+ def __init__(self, minimum, maximum, name=None, latex_label=None,
+ unit=None):
+ """Symmetric Log-Uniform distribtions with bounds
+
+ This is identical to a Log-Uniform distribition, but mirrored about
+ the zero-axis and subsequently normalized. As such, the distribution
+ has support on the two regions [-maximum, -minimum] and [minimum,
+ maximum].
+
+ Parameters
+ ----------
+ minimum: float
+ See superclass
+ maximum: float
+ See superclass
+ name: str
+ See superclass
+ latex_label: str
+ See superclass
+ unit: str
+ See superclass
+ """
+ Prior.__init__(self, name=name, latex_label=latex_label,
+ minimum=minimum, maximum=maximum, unit=unit)
+
+ def rescale(self, val):
+ """
+ 'Rescale' a sample from the unit line element to the power-law prior.
+
+ This maps to the inverse CDF. This has been analytically solved for this case.
+
+ Parameters
+ ----------
+ val: float
+ Uniform probability
+
+ Returns
+ -------
+ float: Rescaled probability
+ """
+ Prior.test_valid_for_rescaling(val)
+ if val < 0.5:
+ return -self.maximum * np.exp(-2 * val * np.log(self.maximum / self.minimum))
+ elif val > 0.5:
+ return self.minimum * np.exp(np.log(self.maximum / self.minimum) * (2 * val - 1))
+ else:
+ raise ValueError("Rescale not valid for val=0.5")
+
+ def prob(self, val):
+ """Return the prior probability of val
+
+ Parameters
+ ----------
+ val: float
+
+ Returns
+ -------
+ float: Prior probability of val
+ """
+ return (
+ np.nan_to_num(0.5 / np.abs(val) / np.log(self.maximum / self.minimum)) *
+ self.is_in_prior_range(val))
+
+ def ln_prob(self, val):
+ """Return the logarithmic prior probability of val
+
+ Parameters
+ ----------
+ val: float
+
+ Returns
+ -------
+ float:
+
+ """
+ return np.nan_to_num(- np.log(2 * np.abs(val)) - np.log(np.log(self.maximum / self.minimum)))
+
+
class Cosine(Prior):
def __init__(self, name=None, latex_label=None, unit=None,
--
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